An evolutionary algorithm for bid-based dynamic economic load dispatch in a deregulated electricity market

The paper presents a novel evolutionary algorithm (EA) approach to solving the bid-based dynamic economic load dispatch problem in a deregulated electricity market. Power generating companies and customers submit bids in advance of each trading transaction, and these bids are matched by the market controller - the 'independent system operator' (ISO), who conducts the dispatch, determines the prevailing market prices and corresponding supply/demand schedules for all generators and customers in a multi-player/multi-period transaction to maximize social profit. The optimization problem faced by the ISO is addressed in very recent literature, and is more realistic than related problems that have been studied in the optimization literature for several years, the so-called static and dynamic economic load dispatch problems (SELD and DELD). In this paper we use an EA with a smart mutation operator (which has proven successful on the SELD and DELD), which focuses mutation on genes contributing most to costs and penalty violations. We find our EA outperforms previous results on the bid-based problem, we also test three versions of the smart mutation operator, and we also define and show results on a new, larger test case for the bid-based problem.